Multi-objective optimization using steady state genetic algorithms

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Abstract

There are many interesting problems in the real world that require multiple objectives to be satisfied at the same time. For many real world design problems, the number of objective evaluations performed is a critical factor as a single objective evaluation can be quite expensive. The aim of our research is to reduce the number of objective evaluations needed to find a well- distributed sampling of the Pareto-optimal region for real world design problems that have many constraints and small feasible regions. One method called OEGADO runs several GAs concurrently with each GA optimizing one objective and exchanging information about its objective with the others. The other method called OSGADO switches attention between objectives periodically. Empirical results in several engineering and benchmark domains comparing our methods with other contemporary GAs suggest that our methods are well suited for solving real-world application problems.